000133235 001__ 133235
000133235 005__ 20250923084430.0
000133235 0247_ $$2doi$$a10.3390/app14051890
000133235 0248_ $$2sideral$$a137945
000133235 037__ $$aART-2024-137945
000133235 041__ $$aeng
000133235 100__ $$aBurillo, Francisco
000133235 245__ $$aReal-Time Production Scheduling and Industrial Sonar and Their Application in Autonomous Mobile Robots
000133235 260__ $$c2024
000133235 5060_ $$aAccess copy available to the general public$$fUnrestricted
000133235 5203_ $$aIn real-time production planning, there are exceptional events that can cause problems and deviations in the production schedule. These circumstances can be solved with real-time production planning, which is able to quickly reschedule the operations at each work centre. Mobile autonomous robots are a key element in this real-time planning and are a fundamental link between production centres. Work centres in Industry 4.0 environments can use current technology, i.e., a biomimetic strategy that emulates echolocation, with the aim of establishing bidirectional communication with other work centres through the application of agile algorithms. Taking advantage of these communication capabilities, the basic idea is to distribute the execution of the algorithm among different work centres that interact like a parasympathetic system that makes automatic movements to reorder the production schedule. The aim is to use algorithms with an optimal solution based on the simplicity of the task distribution, trying to avoid heuristic algorithms or heavy computations. This paper presents the following result: the development of an Industrial Sonar algorithm which allows real-time scheduling and obtains the optimal solution at all times. The objective of this is to reduce the makespan, reduce energy costs and carbon footprint, and reduce the waiting and transport times for autonomous mobile robots using the Internet of Things, cloud computing and machine learning technologies to emulate echolocation.
000133235 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000133235 590__ $$a2.5$$b2024
000133235 592__ $$a0.521$$b2024
000133235 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b50 / 175 = 0.286$$c2024$$dQ2$$eT1
000133235 591__ $$aCHEMISTRY, MULTIDISCIPLINARY$$b123 / 239 = 0.515$$c2024$$dQ3$$eT2
000133235 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b283 / 460 = 0.615$$c2024$$dQ3$$eT2
000133235 591__ $$aPHYSICS, APPLIED$$b101 / 187 = 0.54$$c2024$$dQ3$$eT2
000133235 593__ $$aEngineering (miscellaneous)$$c2024$$dQ2
000133235 593__ $$aComputer Science Applications$$c2024$$dQ2
000133235 593__ $$aProcess Chemistry and Technology$$c2024$$dQ2
000133235 593__ $$aInstrumentation$$c2024$$dQ2
000133235 593__ $$aMaterials Science (miscellaneous)$$c2024$$dQ2
000133235 593__ $$aFluid Flow and Transfer Processes$$c2024$$dQ2
000133235 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000133235 700__ $$0(orcid)0000-0003-1401-6495$$aLambán, María-Pilar$$uUniversidad de Zaragoza
000133235 700__ $$0(orcid)0000-0002-0692-5982$$aRoyo, Jesús-Antonio$$uUniversidad de Zaragoza
000133235 700__ $$aMorella, Paula
000133235 700__ $$aSánchez, Juan-Carlos
000133235 7102_ $$15002$$2515$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Ing. Procesos Fabricación
000133235 773__ $$g14, 5 (2024), 1890 [16 pp.]$$pAppl. sci.$$tApplied Sciences (Switzerland)$$x2076-3417
000133235 8564_ $$s850245$$uhttps://zaguan.unizar.es/record/133235/files/texto_completo.pdf$$yVersión publicada
000133235 8564_ $$s2659347$$uhttps://zaguan.unizar.es/record/133235/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000133235 909CO $$ooai:zaguan.unizar.es:133235$$particulos$$pdriver
000133235 951__ $$a2025-09-22-14:42:32
000133235 980__ $$aARTICLE